Evaluation of risk adjustment performance of diagnosis-based and medication-based comorbidity indices in patients with chronic obstructive pulmonary disease

Objectives This study assessed risk adjustment performance of six comorbidity indices in two categories of comorbidity measures: diagnosis-based comorbidity indices and medication-based ones in patients with chronic obstructive pulmonary disease (COPD). Methods This was a population–based retrospective cohort study. Data used in this study were sourced from the Taiwan National Health Insurance Research Database. The study population comprised all patients who were hospitalized due to COPD for the first time in the target year of 2012. Each qualified patient was individually followed for one year starting from the index date to assess two outcomes of interest, medical expenditures within one year after discharge and in-hospital mortality of patients. To assess how well the added comorbidity measures would improve the fitted model, we calculated the log-likelihood ratio statistic G2. Subsequently, we compared risk adjustment performance of the comorbidity indices by using the Harrell c-statistic measure derived from multiple logistic regression models. Results Analytical results demonstrated that that comorbidity measures were significant predictors of medical expenditures and mortality of COPD patients. Specifically, in the category of diagnosis-based comorbidity indices the Elixhauser index was superior to other indices, while the RxRisk-V index was a stronger predictor in the framework of medication-based codes, for gauging both medical expenditures and in-hospital mortality by utilizing information from the index hospitalization only as well as the index and prior hospitalizations. Conclusions In conclusion, this work has ascertained that comorbidity indices are significant predictors of medical expenditures and mortality of COPD patients. Based on the study findings, we propose that when designing the payment schemes for patients with chronic diseases, the health authority should make adjustments in accordance with the burden of health care caused by comorbid conditions.

Response: First and foremost, we would like to extend to Professor Lim our deep appreciation for your willingness to take over the editorship of our submitted manuscript which was submitted to PLOS ONE on June 3 rd , 2021. In addition, we truly appreciate that you had assigned such qualified reviewers to our manuscript.
Their thoughtful comments and constructive suggestions were a tremendous help to us during our revision process. Thank you so much.
As per your comment on our manuscript meeting PLOS ONE's style requirements, including those for file naming, we have worked diligently to recheck our manuscript with respect to this advice for a couple of times, notwithstanding our manuscript had passed in-house technical check of the journal before. Thank you.

2.
In your ethics statement in the Methods section and in the online submission form, please provide additional information about the data used in your retrospective study. Specifically, please ensure that you have discussed whether all data were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent. If patients provided informed written consent to have data from their medical records used in research, please include this information. The report is well written and clear. The methodology is logical, but leaves a few questions, which if answered, would strengthen the report.
Response: First and foremost, we want to extend our deep appreciation to Reviewer 1 for his/her encouraging remarks and excellent advice on our original submission. We have worked hard to be responsive to the reviewer's valuable comments. After addressing the significant issues raised by the reviewer, we feel that the quality of our manuscript is much improved, and hope the reviewer agrees.
Major comments: 1. Please explain your choice of years for inclusion in more detail. These data are now ≥9 years old. Was newer data not available? Was the choice related to the ICD-9 vs -10? If the latter, there are CCI and ECI algorithms available for ICD-10 as well.

Response:
The reviewer had raised very focal points, and we are greatly appreciative of the opportunity for clarifications. While fully appreciating the fact of not using more recent datasets in this analysis as pointed out by the reviewer, we would also like to ask to consider some facts and limitations. Unfortunately, submission of this manuscript had encountered an unduly lengthy delay between our initial submission and the return of responses from journals. For example, before submitting our manuscript to PLOS ONE, we had submitted it to another journal. After nearly one year of delay (we had sent out inquiries to that journal for numerous times in between), we finally received a reviewing report where it stated succinctly that our paper was not a good fit for that journal's scope.
Actually, we submitted our manuscript to PLOS ONE on June 3 rd , 2021. We had sent out a couple of inquiries to the journal office of PLOS ONE with regard to the status of our submission, and the reply had always been that they had tried assiduously but had difficulty in finding an academic editor to handle our submission (still, I would like to emphasize here that the staff at the journal office had always been acting promptly, professionally, and even empathetically in responding to our inquiries). Hence, we feel deeply grateful to Professor Lim for his willingness to take over the editorship of our submitted manuscript in the end, and truly appreciate that you would accept his invitation to be the reviewer of our manuscript and had provided us with thoughtful comments and constructive suggestions.
With that being said, we understand that this could not constitute a legitimate excuse for your question regarding our choice of data years for this analysis. We acknowledge that we do not utilize more updated datasets. The fact is that this study was supported by the Ministry of Science and Technology in Taiwan  Although the data used in the study are relatively old (we totally agree with the reviewer's observation and opinion), we believe that the research topic is still relevant, and the trend and the pattern of data are mostly similar with respect to risk-adjusted comorbidities in patients with COPD. We hope the reviewer may agree.

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Did you consider fitting a model with both RxRisk-V and ECI since they were the front-runners in model improvement?   [15], the Charlson/D'Hoore index [16], the Charlson/Romano index [17], and the Elixhauser index (EI) [18]. Those  for example, the chronic disease score (CDS) [19], the modified chronic disease score (CDS-2) [20], the RxRisk index [21], and the RxRisk-V index [22]. The CDS, the first pharmacy-based measure of comorbidity, was created by von Korff and colleagues [19]  6. Analysis, discrimination. I suggest to add, for each index, the predicted outcome probability according to index scores, to assess (at least visually) discrimination.

Response:
We appreciate this suggestion from the reviewer. We conducted related statistical analyses and constructed tables 2 and 3 with the current Elixhauser, RxRisk-V and other comorbidity indices, the c-statistics derived from multiple logistic regression models are typically used, as we had done so in our analysis as well. Upon reading the reviewer's suggestion, we have held extensive discussions, but still, we are not quite sure how we could make the statistics of the predicted outcome probability according to index scores fit in with the current tables 2 and 3. Moreover, we are concerned that the revised tables would become too multifaceted to be fittingly interpreted by the reader, then. While we support the referee's assertion that the information pertaining to the predicted outcome